{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test of Resampling Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import neurokit2 as nk\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signal(length=1000, end=20):\n",
    "    signal = np.cos(np.linspace(start=0, stop=end, num=length))\n",
    "    return(signal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample(signal, method=\"interpolation\"):\n",
    "    t0 = time.time()\n",
    "    resampled = nk.signal_resample(\n",
    "        nk.signal_resample(signal,\n",
    "                           sampling_rate=100,\n",
    "                           desired_sampling_rate=50,\n",
    "                           method=method),\n",
    "        sampling_rate=50,\n",
    "        desired_sampling_rate=100,\n",
    "        method=method)\n",
    "    t = (time.time() - t0) * 100\n",
    "    return(resampled, t)\n",
    "    \n",
    "\n",
    "def resample_all(signal):\n",
    "    \n",
    "    interpolation, t_interpolation = resample(signal, \"interpolation\")\n",
    "    sig_numpy, t_numpy = resample(signal, \"numpy\")\n",
    "    fft, t_fft = resample(signal, \"FFT\")\n",
    "    poly, t_poly = resample(signal, \"poly\")\n",
    "    sig_pandas, t_pandas = resample(signal, \"pandas\")\n",
    "    \n",
    "    data = pd.DataFrame({\"Original\": signal,\n",
    "                         \"Interpolation\": interpolation,\n",
    "                         \"Numpy\": sig_numpy,\n",
    "                         \"FFT\": fft,\n",
    "                         \"Poly\": poly,\n",
    "                         \"Pandas\": sig_pandas})\n",
    "    data[\"Signal_Length\"] = len(signal)\n",
    "    \n",
    "    timings = pd.DataFrame({\"Interpolation\": [t_interpolation],\n",
    "                            \"Numpy\": [t_numpy],\n",
    "                            \"FFT\": [t_fft],\n",
    "                            \"Poly\": [t_poly],\n",
    "                            \"Pandas\": [t_pandas]})\n",
    "    timings[\"Signal_Length\"] = len(signal)\n",
    "    return(data, timings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_difference(data):\n",
    "    diff = pd.DataFrame({\"Interpolation\": 1000*(data[\"Original\"].values - data[\"Interpolation\"].values),\n",
    "                         \"Numpy\": 1000*(data[\"Original\"].values - data[\"Numpy\"].values),\n",
    "                         \"FFT\": 1000*(data[\"Original\"].values - data[\"FFT\"].values),\n",
    "                         \"Poly\": 1000*(data[\"Original\"].values - data[\"Poly\"].values),\n",
    "                         \"Pandas\": 1000*(data[\"Original\"].values - data[\"Pandas\"].values)})\n",
    "    return(diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73f3609c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, timing = resample_all(np.cos(np.linspace(start=0, stop=20, num=50)))\n",
    "data.iloc[:,:-1].plot(style='.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73c1d6748>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_difference(data).plot(style='.-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This takes some time\n",
    "timings = []\n",
    "diffs = []\n",
    "for length in range(10, 10**5, 200):\n",
    "    for i in range(10):\n",
    "        end = np.random.randint(1, 5000)\n",
    "        data, timing = resample_all(generate_signal(length, end))\n",
    "        diff = get_difference(data)\n",
    "        diff = pd.DataFrame(diff.mean()).transpose()\n",
    "        diff[\"Signal_Length\"] = data[\"Signal_Length\"][0]\n",
    "        diffs += [diff]\n",
    "        timings += [timing]\n",
    "timings = pd.concat(timings)\n",
    "diffs = pd.concat(diffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73d3e2348>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings.iloc[:,:-1].plot.hist(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interpolation</th>\n",
       "      <th>Numpy</th>\n",
       "      <th>FFT</th>\n",
       "      <th>Poly</th>\n",
       "      <th>Pandas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.843690</td>\n",
       "      <td>0.144137</td>\n",
       "      <td>15.489238</td>\n",
       "      <td>0.264629</td>\n",
       "      <td>0.452339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.500968</td>\n",
       "      <td>0.089351</td>\n",
       "      <td>29.426544</td>\n",
       "      <td>0.138328</td>\n",
       "      <td>0.173831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.398898</td>\n",
       "      <td>0.099707</td>\n",
       "      <td>0.598478</td>\n",
       "      <td>0.198936</td>\n",
       "      <td>0.299305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.797915</td>\n",
       "      <td>0.099826</td>\n",
       "      <td>1.994812</td>\n",
       "      <td>0.299144</td>\n",
       "      <td>0.399137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.296520</td>\n",
       "      <td>0.199485</td>\n",
       "      <td>12.025100</td>\n",
       "      <td>0.398898</td>\n",
       "      <td>0.498915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.791929</td>\n",
       "      <td>0.498700</td>\n",
       "      <td>143.715382</td>\n",
       "      <td>0.797892</td>\n",
       "      <td>6.283164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Interpolation        Numpy          FFT         Poly       Pandas\n",
       "count    5000.000000  5000.000000  5000.000000  5000.000000  5000.000000\n",
       "mean        0.843690     0.144137    15.489238     0.264629     0.452339\n",
       "std         0.500968     0.089351    29.426544     0.138328     0.173831\n",
       "min         0.000000     0.000000     0.000000     0.000000     0.199342\n",
       "25%         0.398898     0.099707     0.598478     0.198936     0.299305\n",
       "50%         0.797915     0.099826     1.994812     0.299144     0.399137\n",
       "75%         1.296520     0.199485    12.025100     0.398898     0.498915\n",
       "max         2.791929     0.498700   143.715382     0.797892     6.283164"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timings.iloc[:,:-1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7414c3988>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings.plot.scatter(x='Signal_Length', y='Interpolation', c=\"blue\")\n",
    "timings.plot.scatter(x='Signal_Length', y='FFT', c=\"green\")\n",
    "timings.plot.scatter(x='Signal_Length', y='Poly', c=\"orange\")\n",
    "timings.plot.scatter(x='Signal_Length', y='Numpy', c=\"red\")\n",
    "timings.plot.scatter(x='Signal_Length', y='Pandas', c=\"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73d624608>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs.iloc[:,:-1].plot.hist(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interpolation</th>\n",
       "      <th>Numpy</th>\n",
       "      <th>FFT</th>\n",
       "      <th>Poly</th>\n",
       "      <th>Pandas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2.005586e-01</td>\n",
       "      <td>1.895393e-01</td>\n",
       "      <td>3.987031e-14</td>\n",
       "      <td>0.085959</td>\n",
       "      <td>2.513644e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7.130335e+00</td>\n",
       "      <td>4.174606e+00</td>\n",
       "      <td>5.661168e-14</td>\n",
       "      <td>1.799357</td>\n",
       "      <td>4.853036e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2.989666e-10</td>\n",
       "      <td>3.630774e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>2.036200e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.234398e-05</td>\n",
       "      <td>1.285746e-04</td>\n",
       "      <td>2.705313e-15</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>3.231978e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.767461e-04</td>\n",
       "      <td>3.964192e-04</td>\n",
       "      <td>1.150782e-14</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>1.015829e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.626458e-03</td>\n",
       "      <td>1.612028e-03</td>\n",
       "      <td>5.468336e-14</td>\n",
       "      <td>0.009181</td>\n",
       "      <td>2.012779e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>4.816906e+02</td>\n",
       "      <td>2.213174e+02</td>\n",
       "      <td>3.261186e-13</td>\n",
       "      <td>73.466829</td>\n",
       "      <td>1.811800e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Interpolation         Numpy           FFT         Poly        Pandas\n",
       "count   5.000000e+03  5.000000e+03  5.000000e+03  5000.000000  5.000000e+03\n",
       "mean    2.005586e-01  1.895393e-01  3.987031e-14     0.085959  2.513644e-01\n",
       "std     7.130335e+00  4.174606e+00  5.661168e-14     1.799357  4.853036e+00\n",
       "min     2.989666e-10  3.630774e-08  0.000000e+00     0.000001  2.036200e-08\n",
       "25%     5.234398e-05  1.285746e-04  2.705313e-15     0.001640  3.231978e-03\n",
       "50%     1.767461e-04  3.964192e-04  1.150782e-14     0.004433  1.015829e-02\n",
       "75%     1.626458e-03  1.612028e-03  5.468336e-14     0.009181  2.012779e-02\n",
       "max     4.816906e+02  2.213174e+02  3.261186e-13    73.466829  1.811800e+02"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffs.iloc[:,:-1].abs().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c741020ec8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GYNu2YilJasogaaSvD045BdauLZZ9fZ2ukSRNWwbJSEND0NsLe/fCnj3FsrfXKxNJasIgGWlgALq6Di1buLAolySNYpCM1NMD+/cfWnbgQFEuSRrFIBmpuxu2bIHFi2HJkmK5ZUtRLkkapW1Pts9oGzfCmjVFd1ZPjyEiSWMwSJrp7jZAJKkFdm1JkioxSCRJlRgkkqRKDBJJUiUGiSSpEoNEklSJQSJJqsQgkSRVYpBIkioxSCRJlRgkkqRKDBJJUiUGiSSpEoNEklSJQSJJqqQjQRIR50fE3RHxTETU6sp7ImJvRNxefj5Rt211RPwgInZGxKUREZ2ouyTpUJ26IrkLeAvwzQbb7s/MM8rPu+vKPw5sAlaWn3Xtr6YkaTwdCZLM3JGZ97W6f0QsBZZk5rczM4FPA+e1rYKSpJZNxzGSFRHx/Yj4RkT8flm2DBis22ewLJMkdVjb3tkeEbcCz2uw6eLMvL7JYbuBkzPz0YhYDVwXEacBjcZDcoxzb6LoBuPkk08+vIpLkg5L24IkM9dM4Jh9wL5yfXtE3A+8kOIKZHndrsuBXWP8zmZgM0CtVmsaOJKk6qZV11ZEdEfE/HL9BRSD6j/OzN3A4xFxdnm31tuBZlc1kqQp1Knbf98cEYPAy4AvRsTWctMrgTsj4g7gc8C7M/Pn5bb3AJ8EdgL3AzdPcbUlSQ1EcRPU7FWr1bK/v7/T1ZCkGSUitmdmbfw9p1nXliRp5jFIJEmVGCSSpEoMEklSJQaJJKkSg0SSVIlBIkmqxCCRJFVikEiSKjFIJEmVGCSSpEoMEklSJQaJJKkSg0SSVIlBIkmqxCCRJFVikEiSKjFIJEmVGCSSpEoMEklSJQaJJKkSg0SSVIlBIkmqpCNBEhF/GxH3RsSdEfGFiDimbttFEbEzIu6LiNfXla+OiB+U2y6NiOhE3SVJh+rUFcmXgRdn5m8DPwQuAoiIVcAG4DRgHXB5RMwvj/k4sAlYWX7WTXWlJUmjdSRIMvOWzHyq/PodYHm5vh64OjP3ZeYDwE7grIhYCizJzG9nZgKfBs6b8opLkkaZDmMkfwLcXK4vAx6s2zZYli0r10eWNxQRmyKiPyL6h4aGJrm6kqR6C9r1wxFxK/C8Bpsuzszry30uBp4CPjN8WIP9c4zyhjJzM7AZoFarNd1PklRd24IkM9eMtT0iLgDOBV5bdldBcaVxUt1uy4FdZfnyBuWSpA7r1F1b64D3A2/KzCfrNt0AbIiIRRGxgmJQ/bbM3A08HhFnl3drvR24fsorLkkapW1XJOO4DFgEfLm8i/c7mfnuzLw7Iq4F7qHo8rowM58uj3kP8ClgMcWYys2jflWSNOU6EiSZ+e/G2HYJcEmD8n7gxe2slyTp8E2Hu7YkSTOYQSJJqsQgkSRVYpBIkioxSCRJlRgkzQwNwbZtxVKS1JRB0khfH5xyCqxdWyz7+jpdI0matgySkYaGoLcX9u6FPXuKZW+vVyaS1IRBMtLAAHR1HVq2cGFRLkkaxSAZqacH9u8/tOzAgaJckjSKQTJSdzds2QKLF8OSJcVyy5aiXJI0SqcmbZzeNm6ENWuK7qyeHkNEksZgkDTT3W2ASFIL7NqSJFVikEiSKjFIJEmVGCSSpEoMEklSJZGZna5DW0XEEPCTCR5+AvDIJFZnJrDNc4Ntnhsm2uZHADJzXSs7z/ogqSIi+jOz1ul6TCXbPDfY5rlhqtps15YkqRKDRJJUiUEyts2drkAH2Oa5wTbPDVPSZsdIJEmVeEUiSarEIGkgItZFxH0RsTMiPtDp+hyuiDgpIr4WETsi4u6IeG9ZflxEfDkiflQuj6075qKyvfdFxOvryldHxA/KbZdGRJTliyLimrL8uxHRM9XtHCki5kfE9yPixvL7rG4vQEQcExGfi4h7y3/fL5vt7Y6I/1T+d31XRPRFxBGzrc0RcWVEPBwRd9WVTUkbI+KC8hw/iogLWqpwZvqp+wDzgfuBFwBdwB3Aqk7X6zDbsBQ4s1w/GvghsAr4CPCBsvwDwP8s11eV7VwErCjbP7/cdhvwMiCAm4F/X5b/KfCJcn0DcM00aPdfAv8M3Fh+n9XtLetyFfDOcr0LOGY2txtYBjwALC6/Xwv8h9nWZuCVwJnAXXVlbW8jcBzw43J5bLl+7Lj17fT/EKbbp/yHvrXu+0XARZ2uV8U2XQ+sBe4DlpZlS4H7GrUR2Fr+c1gK3FtXvhG4on6fcn0BxQNM0cE2Lge+AryGg0Eya9tb1mMJxR/VGFE+a9tNESQPln/oFgA3Aq+bjW0Gejg0SNrexvp9ym1XABvHq6tdW6MN/4c6bLAsm5HKS9aXAt8FTszM3QDl8rnlbs3avKxcH1l+yDGZ+RSwBzi+HW1o0f8G/ivwTF3ZbG4vFFfNQ8D/Kbv0PhkRRzKL252Z/wb8L+CnwG5gT2bewixuc52paOOE/v4ZJKNFg7IZeWtbRBwF/Avwvsx8bKxdG5TlGOVjHTPlIuJc4OHM3N7qIQ3KZkx76yyg6P74eGa+FPgVRZdHMzO+3eW4wHqKLpznA0dGxB+NdUiDshnV5hZMZhsn1HaDZLRB4KS678uBXR2qy4RFxEKKEPlMZn6+LH4oIpaW25cCD5flzdo8WK6PLD/kmIhYADwH+Pnkt6QlLwfeFBEDwNXAayLin5i97R02CAxm5nfL75+jCJbZ3O41wAOZOZSZB4DPA7/H7G7zsKlo44T+/hkko20DVkbEiojoohiIuqHDdTos5Z0ZW4Admfl3dZtuAIbvwriAYuxkuHxDeSfHCmAlcFt5+fx4RJxd/ubbRxwz/FtvBb6aZafqVMvMizJzeWb2UPz7+mpm/hGztL3DMvNnwIMR8aKy6LXAPczudv8UODsifqOs62uBHczuNg+bijZuBV4XEceWV3+vK8vGNtUDSDPhA7yB4k6n+4GLO12fCdT/FRSXo3cCt5efN1D0gX4F+FG5PK7umIvL9t5HeWdHWV4D7iq3XcbBh1iPAD4L7KS4M+QFnW53Wa9Xc3CwfS609wygv/x3fR3FnTazut3A/wDuLev7jxR3K82qNgN9FGNAByiuEnqnqo3An5TlO4F3tFJfn2yXJFVi15YkqRKDRJJUiUEiSarEIJEkVWKQSJIqMUgkSZUYJJpzIuLichryOyPi9oj43XKeqlVtONcTY2zrqZ8mvB0i4n0R8Rut1EeaqAWdroA0lSLiZcC5FNPs74uIE4CuzHxnh6vWLu8D/gl4stMV0ezlFYnmmqXAI5m5DyAzH8nMXRHx9YioAUREb0T8sCz7h4i4rCz/VPlyoG9FxI8j4q1l+VER8ZWI+F75EqH1VSoYEb8ZEV+KiO0R8f8i4rfGOf+8iLi8vMq6MSJuioi3RsRfUExs+LWI+Frd718SEXdExHci4sQqdZXAINHccwtwUhkUl0fEq+o3RsTzgf8GnE3xDpffGnH8UoopaM4FPlyW/Rp4c2aeCZwDfLSc22iiNgN/npmrgf8CXD7O+d9C8e6K04F3UryLgsy8lGLCvXMy85xy3yOB72TmS4BvAv+xQj0lwK4tzTGZ+URErAZ+n+KP/jVx6OuUzwK+kZk/B4iIzwIvrNt+XWY+A9xT9//mA/ibiHglxftQlgEnAj873PqVU///HvDZuixaNM75XwF8tiz/Wf3VRwP7KV4GBbCdIiylSgwSzTmZ+TTwdeDrEfEDDs6CCo3fx1BvX4N93wZ0A6sz80A5nf0RE6zePOCXmXnGYZz/cK5+DuTBCfaexr8BmgR2bWlOiYgXRcTKuqIzgJ/Ufb8NeFU5jfYC4A9a+NnnULxY60BEnAOcMtH6ZfECsgci4vyyvhERLxnnsH8F/qAcKzmRYgbkYY8DR0+0PlIrDBLNNUcBV0XEPRFxJ7AK+NDwxixe5fo3FK8mvpXi/R57xvnNzwC1iOinuDq59zDq86KIGKz7nF/+Rm9E3AHcTfFGwLH8C8VU43dRvGP7u3V13gzcPE53l1SJ08hLI0TEUeVYygLgC8CVmfmFTtdrLHV1Pp7iqurlWbz4Smo7+0el0T4UEWsoxjluoXhh1HR3Y0QcA3QBf22IaCp5RSK1WUScTvEmv3r7MvN3O1EfabIZJJKkShxslyRVYpBIkioxSCRJlRgkkqRKDBJJUiX/H5nYXBcBoHzUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs.plot.scatter(x='Signal_Length', y='Interpolation', c=\"blue\")\n",
    "diffs.plot.scatter(x='Signal_Length', y='FFT', c=\"green\")\n",
    "diffs.plot.scatter(x='Signal_Length', y='Poly', c=\"orange\")\n",
    "diffs.plot.scatter(x='Signal_Length', y='Numpy', c=\"red\")\n",
    "diffs.plot.scatter(x='Signal_Length', y='Pandas', c=\"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
